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This algorithm type is thus useful for analyzing large whole slide images, where you are e.g., interested in the counting objects. Object detection algorithms are widely used for identifying objects in images and separating them for further analysis. For instance, this is a standard technique for recognizing faces in digital camera applications — or even smiling faces in views.

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📑 Instance segmentation

Combining object detection and semantic segmentation capabilities, instance segmentation is one of the more versatile training types. Just like semantic segmentation, an instance segmentation algorithm training yields a pixel-wise map assigning each pixel in the image to one of the detected labels. At the same time it provides a bounding box location for all individual instances of this object class shown in the image.

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Figure 4 features two ladybugs. If this image was to be analyzed with semantic segmentation only, one could easily differentiate between pixels containing ladybugs, leaves or the background (as can be seen in Figure 5).

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However, semantic segmentation will not allow to easily differentiate between the positions of the two depicted ladybugs. As the two are touching, there is no straightforward way to know even that there are two ladybugs in this picture based on semantic segmentation alone.

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Instance segmentation extends the functionality of semantic segmentation by adding object detection to the analysis. In addition to yielding a segmentation map, an instance segmentation algorithm will also provide information on the locations and sizes of the bounding boxes of each object instance depicted in the image.

This allows for further analysis on the instance-level such as counting instances, measuring distances between instances or examining the area covered with (or even surrounding the) instances.

Now you know everything about the various algorithm training types.

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